Risk Measures and Insurance Solvency Benchmarks: Fixed-Probability Levels in Renewal Risk Models

— —

Vsevolod K. Malinovskii

Risk Measures and Insurance Solvency Benchmarks

Fixed-Probability Levels in Renewal Risk Models

Vsevolod K. Malinovskii

$96.99

Paperback

Not in-store but you can order this
How long will it take?

Availability Information

We source books from suppliers in Australia and overseas. For books we don't currently have in stock, the time it takes to get them from our suppliers can vary widely - from a few days to a few months - so we check each book with each supplier to determine the expected time it will take to be supplied to us.

We then advise you accordingly. If the time taken to get any book is too long for you, you can let us know and we will cancel or adjust your order, and refund as required.

To find out the anticipated arrival time for specific items prior to ordering, please contact us by phone or email:

Phone +61 2 9264 3111, or 1800 4 BOOKS (1800 4 26657) if outside Sydney:
option 1 Abbey's Bookshop (Crime, History, Science, Kids & more) • info@abbeys.com.au
option 2 Language Book Centre (ESL & Foreign Languages) • language@abbeys.com.au
option 3 Galaxy Bookshop (Sci-fi, Fantasy, Romance, Graphic Novels) • sf@galaxybooks.com.au

QTY:

English

Chapman & Hall/CRC
24 July 2023

Economic statistics; Calculus & mathematical analysis; Probability & statistics; Applied mathematics

Series: Chapman and Hall/CRC Financial Mathematics Series

Risk Measures and Insurance Solvency Benchmarks: Fixed-Probability Levels in Renewal Risk Models is written for academics and practitioners who are concerned about potential weaknesses of the Solvency II regulatory system. It is also intended for readers who are interested in pure and applied probability, have a taste for classical and asymptotic analysis, and are motivated to delve into rather intensive calculations.

The formal prerequisite for this book is a good background in analysis. The desired prerequisite is some degree of probability training, but someone with knowledge of the classical real-variable theory, including asymptotic methods, will also find this book interesting. For those who find the proofs too complicated, it may be reassuring that most results in this book are formulated in rather elementary terms. This book can also be used as reading material for basic courses in risk measures, insurance mathematics, and applied probability. The material of this book was partly used by the author for his courses in several universities in Moscow, Copenhagen University, and in the University of Montreal.

Features

Requires only minimal mathematical prerequisites in analysis and probability

Suitable for researchers and postgraduate students in related fields

Could be used as a supplement to courses in risk measures, insurance mathematics and applied probability.

By: Vsevolod K. Malinovskii
Imprint: Chapman & Hall/CRC
Country of Publication: United Kingdom
Dimensions: Height: 234mm, Width: 156mm,
Weight: 453g
ISBN: 9780367744021
ISBN 10: 0367744023
Series: Chapman and Hall/CRC Financial Mathematics Series
Pages: 340
Publication Date: 24 July 2023
Audience: General/trade , ELT Advanced
Format: Paperback
Publisher's Status: Active

Vsevolod K. Malinovskii graduated from the Moscow State University, earned his Ph.D. in Mathematics from the Steklov Mathematical Institute in 1983, and his D.Sc. in Mathematics from the Central conomics and Mathematics Institute (CEMI) of the Russian Academy of Science in 2000. He joined Probability Theory's Department of Steklov Mathematical Institute in 1982 and worked there until 2006. Since 2009, he has been a Chief research fellow at the CEMI. He was Visiting Professor at the University of Copenhagen in 1993 and in 1998, and at the University of Montreal in 2001. He has authored Insurance Planning Models: Price Competition and Regulation of Financial Stability and Level-Crossing Problems and Inverse Gaussian Distributions: Closed-Form Results and Approximations. Professor Malinovskii's main research interests are in Applied Probability and in Mathematical Statistics.